Is Sphinx Tags Safe?
Sphinx Tags — Nerq Trust Score 55.0/100 (C grade). Score based on 2 independent trust signals. Last analyzed: 2026-11-01
Sphinx Tags is a Python package with a Nerq Trust Score of 55.0/100 (C), based on 3 independent data dimensions. Last analyzed: 2026-11-01 Security: 90/100. Popularity: 45/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-11-01. Machine-readable data (JSON).
Is Sphinx Tags safe?
Trust Score Breakdown — Sphinx Tags has a Nerq Trust Score of 55.0/100 (C). Measured across 2 independent trust signals (as of 2026-11-01).
What is Sphinx Tags's trust score?
Sphinx Tags has a Nerq Trust Score of 55.0/100, earning a C grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Sphinx Tags?
Sphinx Tags's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Sphinx Tags and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Sphinx Tags
What is Sphinx Tags?
Sphinx Tags is a Python package — Sphinx extension to create tags for documentation pages..
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=sphinx-tags
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Sphinx Tags has a Nerq Trust Score of 55/100 (C). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Sphinx Tags has a Trust Score of 55/100 (C).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.